Efficient Biometric Identification on the Cloud With Privacy Preservation Guarantee

Abstract

Benefited from its reliability and convenience, biometric identification has become one of the most popular authentication technologies. Due to the sensitivity of biometric data, various privacy-preserving biometric identification protocols have been proposed. However, the low computational efficiency or the security vulnerabilities of these protocols limit their wide deployment in practice. To further improve the efficiency and enhance the security, in this paper, we propose two new privacy-preserving biometric identification outsourcing protocols. One mainly utilizes the efficient Householder transformation and permutation technique to realize the high-efficiency intention under the known candidate attack model. The other initializes a novel random split technique and combines it with the invertible linear transformation to achieve a higher security requirement under the known-plaintext attack model. Also, we argue the security of our proposed two protocols with a strict theoretical analysis and, by comparing them with the prior existing works, comprehensively evaluate their efficiency.